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Per-Unit Sequence Models01:26

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Special Issue on Rate Distortion Theory and Information Theory.

Entropy (Basel, Switzerland)·2020
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Mutual Information Gain and Linear/Nonlinear Redundancy for Agent Learning, Sequence Analysis, and Modeling.

Jerry D Gibson1

  • 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

Intelligent agents can now quantify sequence structure and randomness using relative entropy. New mutual information gain measures capture learning in sequence modeling and analysis.

Keywords:
agent learninglinear redundancymutual information gainnonlinear redundancy

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Area of Science:

  • Information theory
  • Machine learning
  • Signal processing

Background:

  • Intelligent agents require understanding environmental structure and randomness.
  • Distinguishing between linear and nonlinear patterns is crucial for appropriate responses.

Purpose of the Study:

  • To introduce novel methods for quantifying linear and nonlinear redundancy in sequences.
  • To develop new metrics for analyzing sequence structure and apparent randomness.
  • To demonstrate the utility of these metrics in sequence modeling and analysis.

Main Methods:

  • Utilizing relative entropy to differentiate and measure linear and nonlinear redundancy.
  • Introducing total mutual information gain and incremental mutual information gain.
  • Applying these measures to autoregressive sequences and speech signals.

Main Results:

  • Successfully separated and quantified linear and nonlinear redundancy.
  • Demonstrated the effectiveness of mutual information gain for analyzing sequence structures.
  • Validated the approach on both synthetic and real-world (speech) data.

Conclusions:

  • Mutual information gain is a powerful new tool for quantifying learning in sequence modeling.
  • The developed methods enhance the ability of intelligent agents to understand complex environments.
  • This work provides a robust framework for analyzing sequence data with varying degrees of structure and randomness.